LncRNA:DNA triplex-forming sites are positioned at specific areas of genome organization and are predictors for Topologically Associated Domains.

BACKGROUND: Chromosomes are organized into units called topologically associated domains (TADs). TADs dictate regulatory landscapes and other DNA-dependent processes. Even though various factors that contribute to the specification of TADs have been proposed, the mechanism is not fully understood. Understanding the process for specification and maintenance of these units is essential in dissecting cellular processes and disease mechanisms. RESULTS: In this study, we report a genome-wide study that considers the idea of long noncoding RNAs (lncRNAs) mediating chromatin organization using lncRNA:DNA triplex-forming sites (TFSs). By analyzing the TFSs of expressed lncRNAs in multiple cell lines, we find that they are enriched in TADs, their boundaries, and loop anchors. However, they are evenly distributed across different regions of a TAD showing no preference for any specific portions within TADs. No relationship is observed between the locations of these TFSs and CTCF binding sites. However, TFSs are located not just in promoter regions but also in intronic, intergenic, and 3'UTR regions. We also show these triplex-forming sites can be used as predictors in machine learning models to discriminate TADs from other genomic regions. Finally, we compile a list of important "TAD-lncRNAs" which are top predictors for TADs identification. CONCLUSIONS: Our observations advocate the idea that lncRNA:DNA TFSs are positioned at specific areas of the genome organization and are important predictors for TADs. LncRNA:DNA triplex formation most likely is a general mechanism of action exhibited by some lncRNAs, not just for direct gene regulation but also to mediate 3D chromatin organization.

Accurate prediction of boundaries of high resolution topologically associated domains (TADs) in fruit flies using deep learning.

Genomes are organized into self-interacting chromatin regions called topologically associated domains (TADs). A significant number of TAD boundaries are shared across multiple cell types and conserved across species. Disruption of TAD boundaries may affect the expression of nearby genes and could lead to several diseases. Even though detection of TAD boundaries is important and useful, there are experimental challenges in obtaining high resolution TAD locations. Here, we present computational prediction of TAD boundaries from high resolution Hi-C data in fruit flies. By extensive exploration and testing of several deep learning model architectures with hyperparameter optimization, we show that a unique deep learning model consisting of three convolution layers followed by a long short-term-memory layer achieves an accuracy of 96%. This outperforms feature-based models' accuracy of 91% and an existing method's accuracy of 73-78% based on motif TRAP scores. Our method also detects previously reported motifs such as Beaf-32 that are enriched in TAD boundaries in fruit flies and also several unreported motifs.

Transient activation of AMPK preceding left ventricular pressure overload reduces adverse remodeling and preserves left ventricular function.

Coordinated changes in signaling pathways and gene expression in hearts subjected to prolonged stress maintain cardiac function. Loss of steroid receptor coactivator-2 (SRC-2) results in a reversal to the fetal gene program and disrupts the response to pressure overload, accompanied by prominent effects on metabolism and growth signaling, including increased AMPK activation. We proposed that early metabolic stress driven by AMPK activation induces contractile dysfunction in mice lacking SRC-2. We used 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) to activate AMPK transiently before transverse aortic constriction (TAC) in wild-type and cardiomyocyte-specific SRC-2 knockout (CKO) animals. In contrast to AMPK activities during stress, in unstressed hearts, AICAR induced a mild activation of Akt signaling, and, in SRC-2-CKO mice, partially relieved an NAD+ deficiency and increased antioxidant signaling. These molecular changes translated to a mild hypertrophic response to TAC with decreased maladaptive remodeling, including markedly decreased fibrosis. Additionally, preactivation of AMPK in SRC-2-CKO mice was accompanied by a dramatic improvement in cardiac function compared with saline-treated SRC-2-CKO mice. Our results show that altered molecular signaling before stress onset has extended effects on sustained cardiac stress responses, and prestress modulation of transient growth and metabolism pathways may control those effects.-Nam, D. H., Kim, E., Benham, A., Park, H.-K., Soibam, B., Taffet, G. E., Kaelber, J. T., Suh, J. H., Taegtmeyer, H., Entman, M. L., Reineke, E. L. Transient activation of AMPK preceding left ventricular pressure overload reduces adverse remodeling and preserves left ventricular function.

H19X-encoded miR-424(322)/-503 cluster: emerging roles in cell differentiation, proliferation, plasticity and metabolism.

miR-424(322)/-503 are mammal-specific members of the extended miR-15/107 microRNA family. They form a co-expression network with the imprinted lncRNA H19 in tetrapods. miR-424(322)/-503 regulate fundamental cellular processes including cell cycle, epithelial-to-mesenchymal transition, hypoxia and other stress response. They control tissue differentiation (cardiomyocyte, skeletal muscle, monocyte) and remodeling (mammary gland involution), and paradoxically participate in tumor initiation and progression. Expression of miR-424(322)/-503 is governed by unique mechanisms involving sex hormones. Here, we summarize current literature and provide a primer for future endeavors.

HIRA deficiency in muscle fibers causes hypertrophy and susceptibility to oxidative stress.

Nucleosome assembly proceeds through DNA replication-coupled or replication-independent mechanisms. For skeletal myocytes, whose nuclei have permanently exited the cell cycle, replication-independent assembly is the only mode available for chromatin remodeling. For this reason, any nucleosome composition alterations accompanying transcriptional responses to physiological signals must occur through a DNA replication-independent pathway. HIRA is the histone chaperone primarily responsible for replication-independent incorporation of histone variant H3.3 across gene bodies and regulatory regions. Thus, HIRA would be expected to play an important role in epigenetically regulating myocyte gene expression. The objective of this study was to determine the consequence of eliminating HIRA from mouse skeletal myocytes. At 6 weeks of age, myofibers lacking HIRA showed no pathological abnormalities; however, genes involved in transcriptional regulation were downregulated. By 6 months of age, myofibers lacking HIRA exhibited hypertrophy, sarcolemmal perforation and oxidative damage. Genes involved in muscle growth and development were upregulated, but those associated with responses to cellular stresses were downregulated. These data suggest that elimination of HIRA produces a hypertrophic response in skeletal muscle and leaves myofibers susceptible to stress-induced degeneration.

Super-lncRNAs: identification of lncRNAs that target super-enhancers via RNA:DNA:DNA triplex formation.

Super-enhancers are characterized by high levels of Mediator binding and are major contributors to the expression of their associated genes. They exhibit high levels of local chromatin interactions and a higher order of local chromatin organization. On the other hand, lncRNAs can localize to specific DNA sites by forming a RNA:DNA:DNA triplex, which in turn can contribute to local chromatin organization. In this paper, we characterize a new class of lncRNAs called super-lncRNAs that target super-enhancers and which can contribute to the local chromatin organization of the super-enhancers. Using a logistic regression model based on the number of RNA:DNA:DNA triplex sites a lncRNA forms within the super-enhancer, we identify 442 unique super-lncRNA transcripts in 27 different human cell and tissue types; 70% of these super-lncRNAs were tissue restricted. They primarily harbor a single triplex-forming repeat domain, which forms an RNA:DNA:DNA triplex with multiple anchor DNA sites (originating from transposable elements) within the super-enhancers. Super-lncRNAs can be grouped into 17 different clusters based on the tissue or cell lines they target. Super-lncRNAs in a particular cluster share common short structural motifs and their corresponding super-enhancer targets are associated with gene ontology terms pertaining to the tissue or cell line. Super-lncRNAs may use these structural motifs to recruit and transport necessary regulators (such as transcription factors and Mediator complexes) to super-enhancers, influence chromatin organization, and act as spatial amplifiers for key tissue-specific genes associated with super-enhancers.

miR-322/-503 cluster is expressed in the earliest cardiac progenitor cells and drives cardiomyocyte specification.

Understanding the mechanisms of early cardiac fate determination may lead to better approaches in promoting heart regeneration. We used a mesoderm posterior 1 (Mesp1)-Cre/Rosa26-EYFP reporter system to identify microRNAs (miRNAs) enriched in early cardiac progenitor cells. Most of these miRNA genes bear MESP1-binding sites and active histone signatures. In a calcium transient-based screening assay, we identified miRNAs that may promote the cardiomyocyte program. An X-chromosome miRNA cluster, miR-322/-503, is the most enriched in the Mesp1 lineage and is the most potent in the screening assay. It is specifically expressed in the looping heart. Ectopic miR-322/-503 mimicking the endogenous temporal patterns specifically drives a cardiomyocyte program while inhibiting neural lineages, likely by targeting the RNA-binding protein CUG-binding protein Elav-like family member 1 (Celf1). Thus, early miRNAs in lineage-committed cells may play powerful roles in cell-fate determination by cross-suppressing other lineages. miRNAs identified in this study, especially miR-322/-503, are potent regulators of early cardiac fate.

MicroRNAs and ectodermal specification I. Identification of miRs and miR-targeted mRNAs in early anterior neural and epidermal ectoderm.

The establishment of cell lineages occurs via a dynamic progression of gene regulatory networks (GRNs) that underlie developmental commitment and differentiation. To investigate how microRNAs (miRs) function in this process, we compared miRs and miR targets at the initiation of the two major ectodermal lineages in Xenopus. We used next-generation sequencing to identify over 170 miRs expressed in midgastrula ectoderm expressing either noggin or a constitutively active BMP receptor, reflecting anterior neural or epidermal ectoderm, respectively; 125 had not previously been identified in Xenopus. We identified the locations of the pre-miR sequences in the X. laevis genome. Neural and epidermal ectoderm express broadly similar sets of miRs. To identify targets of miR-dependent translational control, we co-immunoprecipitated Argonaute-Ribonucleoprotein (Ago-RNP) complexes from early neural and epidermal ectoderm and sequenced the associated RNA. The Ago-RNP RNAs from these tissues represent overlapping, yet distinct, subsets of genes. Moreover, the profile of Ago-RNP associated genes differs substantially from the profile of total RNAs in these tissues. We generated target predictions for the "high confidence" Ago-RNP RNAs using the identified ectodermal miRs; These RNAs generally had target sites for multiple miRs. Oct4 orthologues, as well as many of their previously identified transcriptional targets, are represented in the Ago-RNP pool in both tissues, suggesting that miR-dependent regulation contributes to the downregulation of the oct4 gene regulatory network and the reduction in ectodermal pluripotency.

Predictors of breast cancer cell types and their prognostic power in breast cancer patients.

BACKGROUND: Comprehensive understanding of intratumor heterogeneity requires identification of molecular markers, which are capable of differentiating different subpopulations and which also have clinical significance. One important tool that has been addressing this issue is single cell RNA-Sequencing (scRNASeq) that allows the quantification of expression profiles of transcripts in individual cells in a population of cancer cells. Using the expression profiles from scRNASeq, current studies conduct analysis to group cells into different subpopulations using clustering algorithms. In this study, we explore scRNASeq cancer data from a different perspective. We focus on scRNASeq data originating from cancer cells pertaining to a particular cancer type, where the cell type or the subpopulation to which each cell belongs is known. We investigate if the "cell type" of a cancer cell can be predicted based on the expression profiles of a small set of transcripts. RESULTS: We outline a predictive analytics pipeline to accurately predict 6 breast cancer cell types using single cell gene expression profiles. Instead of building predictive models using the complete human transcripts, the pipeline first eliminates predictors with low expression and low variance. A multinomial penalized logistic regression further reduces the size of the predictors to only 308, out of which 34 are long non-coding RNAs. Tuning of predictive models shows support vector machines and neural networks as the most accurate models achieving close to 98% prediction accuracies. We also find that mixture of protein coding genes and long non-coding RNAs are better predictors compared to when the two sets of transcripts are treated separately. A signature risk score originating from 65 protein coding genes and 5 lncRNA predictors is associated with prognostic survival of TCGA breast cancer patients. This association was maintained when the risk scores were generated using 65 PCGs and 5 lncRNA separately. We further show that predictors restricted to a particular cell type serve as better prognostic markers for the respective patient subtype. CONCLUSION: Our results show that in general, the breast cancer cell type predictors are also associated with patient survivability and hence have clinical significance.

Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs.

Long noncoding RNAs (lncRNAs) can exert their function by interacting with the DNA via triplex structure formation. Even though this has been validated with a handful of experiments, a genome-wide analysis of lncRNA-DNA binding is needed. In this paper, we develop and interpret deep learning models that predict the genome-wide binding sites deciphered by ChIRP-Seq experiments of 12 different lncRNAs. Among the several deep learning architectures tested, a simple architecture consisting of two convolutional neural network layers performed the best suggesting local sequence patterns as determinants of the interaction. Further interpretation of the kernels in the model revealed that these local sequence patterns form triplex structures with the corresponding lncRNAs. We uncovered several novel triplexes forming domains (TFDs) of these 12 lncRNAs and previously experimentally verified TFDs of lncRNAs HOTAIR and MEG3. We experimentally verified such two novel TFDs of lncRNAs HOTAIR and TUG1 predicted by our method (but previously unreported) using Electrophoretic mobility shift assays. In conclusion, we show that simple deep learning architecture can accurately predict genome-wide binding sites of lncRNAs and interpretation of the models suggest RNA:DNA:DNA triplex formation as a viable mechanism underlying lncRNA-DNA interactions at genome-wide level.

Identification of microRNAs and microRNA targets in Xenopus gastrulae: The role of miR-26 in the regulation of Smad1.

MicroRNAs (miRNAs) are known to play diverse roles in the regulation of vertebrate development. To investigate miRNA-target mRNA relationships in embryonic development, we have carried out small-RNA sequencing to identify miRNAs expressed in the early gastrula of Xenopus laevis. We identify a total of 180 miRNAs, and we have identified the locations of the miRNA precursor sequences in the X. laevis genome. Of these miRNAs, 141 represent miRs previously identified in Xenopus tropicalis. Alignment to human miRNAs led to the identification of 39 miRNAs that have not previously been described for Xenopus. We have also used a biochemical approach to isolate mRNAs that are associated with the RNA-Induced Silencing Complex (RISC) in early gastrulae and thus candidate targets of miRNA-dependent regulation. Interrogation of this RISC-associated mRNA pool by RT-PCR indicates that a number of genes essential for early patterning and specification may be under regulation by miRNAs. Smad1 transcripts are associated with the RISC; target prediction algorithms identify a single miRNA-binding site for miR-26, which is common to the 3'UTRs of Smad1a and Smad1b. Disruption of the interaction between miR-26 and the Smad1 3'UTR via a Target Protector Morpholino Oligonucleotide (TPMO) leads to a 2-fold increase in Smad1 protein accumulation, moderate increases in the expression of BMP4/Smad1 target genes, and a reduction in organizer gene expression, as well as a partially ventralized phenotype in approximately 25% of embryos. Overexpression of miR-26 resulted in moderately decreased expression of Smad1-dependent genes and an expansion of the region expressing the Organizer gene not1. Our findings indicate that interactions between miR-26 and the Smad1 3'UTR modulate Smad1 function in the establishment of axial patterning; they also establish a foundation for the functional analysis of miRNAs and their regulatory interactions during gastrulation.

Genome-Wide Identification of MESP1 Targets Demonstrates Primary Regulation Over Mesendoderm Gene Activity.

MESP1 is considered the first sign of the nascent cardiac mesoderm and plays a critical role in the appearance of cardiac progenitors, while exhibiting a transient expression in the developing embryo. We profiled the transcriptome of a pure population of differentiating MESP1-marked cells and found that they chiefly contribute to the mesendoderm lineage. High-throughput sequencing of endogenous MESP1-bound DNA revealed that MESP1 preferentially binds to two variants of E-box sequences and activates critical mesendoderm modulators, including Eomes, Gata4, Wnt5a, Wnt5b, Mixl1, T, Gsc, and Wnt3. These mesendoderm markers were enriched in the MESP1 marked population before the appearance of cardiac progenitors and myocytes. Further, MESP1-binding is globally associated with H(3)K(27) acetylation, supporting a novel pivotal role of it in regulating target gene epigenetics. Therefore, MESP1, the pioneer cardiac factor, primarily directs the appearance of mesendoderm, the intermediary of the earliest progenitors of mesoderm and endoderm organogenesis.

Steroid receptor coactivator-2 is a dual regulator of cardiac transcription factor function.

We have previously demonstrated the potential role of steroid receptor coactivator-2 (SRC-2) as a co-regulator in the transcription of critical molecules modulating cardiac function and metabolism in normal and stressed hearts. The present study seeks to extend the previous information by demonstrating SRC-2 fulfills this role by serving as a critical coactivator for the transcription and activity of critical transcription factors known to control cardiac growth and metabolism as well as in their downstream signaling. This knowledge broadens our understanding of the mechanism by which SRC-2 acts in normal and stressed hearts and allows further investigation of the transcriptional modifications mediating different types and degrees of cardiac stress. Moreover, the genetic manipulation of SRC-2 in this study is specific for the heart and thereby eliminating potential indirect effects of SRC-2 deletion in other organs. We have shown that SRC-2 is critical to transcriptional control modulated by MEF2, GATA-4, and Tbx5, thereby enhancing gene expression associated with cardiac growth. Additionally, we describe SRC-2 as a novel regulator of PPARα expression, thus controlling critical steps in metabolic gene expression. We conclude that through regulation of cardiac transcription factor expression and activity, SRC-2 is a critical transcriptional regulator of genes important for cardiac growth, structure, and metabolism, three of the main pathways altered during the cardiac stress response.

PySmooth: a Python tool for the removal and correction of genotyping errors.

In genetic mapping studies involving many individuals, genome-wide markers such as single nucleotide polymorphisms (SNPs) can be detected using different methods. However, it comes with some errors. Some SNPs associated with diseases can be in regions encoding long noncoding RNAs (lncRNAs). Therefore, identifying the errors in genotype file and correcting them is crucial for accurate genetic mapping studies. We develop a Python tool called PySmooth, that offers an easy-to-use command line interface for the removal and correction of genotyping errors. PySmooth uses the approach of a previous tool called SMOOTH with some modifications. It inputs a genotype file, detects errors and corrects them. PySmooth provides additional features such as imputing missing data, better user-friendly usage, generates summary and visualization files, has flexible parameters, and handles more genotype codes. AVAILABILITY AND IMPLEMENTATION: PySmooth is available at https://github.com/lncRNAAddict/PySmooth .

ChromNetMotif: a Python tool to extract chromatin-sate marked motifs in a chromatin interaction network.

Motivation: Analysis of network motifs is crucial to studying the robustness, stability, and functions of complex networks. Genome organization can be viewed as a biological network that consists of interactions between different chromatin regions. These interacting regions are also marked by epigenetic or chromatin states which can contribute to the overall organization of the chromatin and proper genome function. Therefore, it is crucial to integrate the chromatin states of the nodes when performing motif analysis in chromatin interaction networks. Even though there has been increasing production of chromatin interaction and genome-wide epigenetic modification data, there is a lack of publicly available tools to extract chromatin state-marked motifs from genome organization data. Results: We develop a Python tool, ChromNetMotif, offering an easy-to-use command line interface to extract chromatin-state-marked motifs from a chromatin interaction network. The tool can extract occurrences, frequencies, and statistical enrichment of the chromatin state-marked motifs. Visualization files are also generated which allow the user to interpret the motifs easily. ChromNetMotif also allows the user to leverage the features of a multicore processor environment to reduce computation time for larger networks. The output files generated can be used to perform further downstream analysis. ChromNetMotif aims to serve as an important tool to comprehend the interplay between epigenetics and genome organization. Availability and implementation: ChromNetMotif is available at https://github.com/lncRNAAddict/ChromNetworkMotif.

Drosophila genotypes can be predicted from their exploration locomotive trajectories using supervised machine learning.

This study employs supervised machine learning algorithms to test whether locomotive features during exploratory activity in open field arenas can serve as predictors for the genotype of fruit flies. Because of the nonlinearity in locomotive trajectories, traditional statistical methods that are used to compare exploratory activity between genotypes of fruit flies may not reveal all insights. 10-minute-long trajectories of four different genotypes of fruit flies in an open-field arena environment were captured. Turn angles and step size features extracted from the trajectories were used for training supervised learning models to predict the genotype of the fruit flies. Using the first five minute locomotive trajectories, an accuracy of 83% was achieved in differentiating wild-type flies from three other mutant genotypes. Using the final 5 min and the entire ten minute duration decreased the performance indicating that the most variations between the genotypes in their exploratory activity are exhibited in the first few minutes. Feature importance analysis revealed that turn angle is a better predictor than step size in predicting fruit fly genotype. Overall, this study demonstrates that features of trajectories can be used to predict the genotype of fruit flies through supervised machine learning methods.

Integrated analysis of microRNA expression and mRNA transcriptome in lungs of avian influenza virus infected broilers.

BACKGROUND: Avian influenza virus (AIV) outbreaks are worldwide threats to both poultry and humans. Our previous study suggested microRNAs (miRNAs) play significant roles in the regulation of host response to AIV infection in layer chickens. The objective of this study was to test the hypothesis if genetic background play essential role in the miRNA regulation of AIV infection in chickens and if miRNAs that were differentially expressed in layer with AIV infection would be modulated the same way in broiler chickens. Furthermore, by integrating with parallel mRNA expression profiling, potential molecular mechanisms of host response to AIV infection can be further exploited. RESULTS: Total RNA isolated from the lungs of non-infected and low pathogenic H5N3 infected broilers at four days post-infection were used for both miRNA deep sequencing and mRNA microarray analyses. A total of 2.6 M and 3.3 M filtered high quality reads were obtained from infected and non-infected chickens by Solexa GA-I Sequencer, respectively. A total of 271 miRNAs in miRBase 16.0 were identified and one potential novel miRNA was discovered. There were 121 miRNAs differentially expressed at the 5% false discovery rate by Fisher's exact test. More miRNAs were highly expressed in infected lungs (108) than in non-infected lungs (13), which was opposite to the findings in layer chickens. This result suggested that a different regulatory mechanism of host response to AIV infection mediated by miRNAs might exist in broiler chickens. Analysis using the chicken 44 K Agilent microarray indicated that 508 mRNAs (347 down-regulated) were differentially expressed following AIV infection. CONCLUSIONS: A comprehensive analysis combining both miRNA and targeted mRNA gene expression suggests that gga-miR-34a, 122-1, 122-2, 146a, 155, 206, 1719, 1594, 1599 and 451, and MX1, IL-8, IRF-7, TNFRS19 are strong candidate miRNAs or genes involved in regulating the host response to AIV infection in the lungs of broiler chickens. Further miRNA or gene specific knock-down assay is warranted to elucidate underlying mechanism of AIV infection regulation in the chicken.

Association between Triplex-Forming Sites of Cardiac Long Noncoding RNA GATA6-AS1 and Chromatin Organization.

This study explored the relationship between 3D genome organization and RNA-DNA triplex-forming sites of long noncoding RNAs (lncRNAs), a group of RNAs that do not code for proteins but are important factors regulating different aspects of genome activity. The triplex-forming sites of anti-sense cardiac lncRNA GATA6-AS1 derived from DBD-Capture-Seq were examined and compared to modular features of 3D genome organization called topologically associated domains (TADs) obtained from Hi-C data. It was found that GATA6-AS1 triplex-forming sites are positioned non-randomly in TADs and their boundaries. The triplex sites showed a preference for TAD boundaries over internal regions of TADs. Computational prediction analysis indicated that CTCF, the key protein involved in TAD specification, may interact with GATA6-AS1, and their binding sites correlate with each other. Examining locations of repeat elements in the genome suggests that the ability of lncRNA GATA6-AS1 to form triplex sites with many genomic locations may be achieved by the rapid expansion of different repeat elements. Some of the triplex-forming sites were found to be positioned in regions that undergo dynamic chromatin organization events such as loss/gain of TAD boundaries during cardiac differentiation. These observed associations suggest that lncRNA-DNA triplex formation may contribute to the specification of TADs in 3D genome organization.

Proinflammatory role for let-7 microRNAS in experimental asthma.

MicroRNAs (miRNAs) are short, non-coding RNAs that target and silence protein coding genes through 3'-UTR elements. Evidence increasingly assigns an immunosuppressive role for miRNAs in immunity, but relatively few miRNAs have been studied, and an overall understanding of the importance of these regulatory transcripts in complex in vivo systems is lacking. Here we have applied multiple technologies to globally analyze miRNA expression and function in allergic lung disease, an experimental model of asthma. Deep sequencing and microarray analyses of the mouse lung short RNAome revealed numerous extant and novel miRNAs and other transcript classes. Similar to mRNAs, lung miRNA expression changed dynamically during the transition from the naive to the allergic state, suggesting numerous functional relationships. A possible role for miRNA editing in altering the lung mRNA target repertoire was also identified. Multiple members of the highly conserved let-7 miRNA family were the most abundant lung miRNAs, and we confirmed in vitro that interleukin 13 (IL-13), a cytokine essential for expression for allergic lung disease, is regulated by mmu-let-7a. However, inhibition of let-7 miRNAs in vivo using a locked nucleic acid profoundly inhibited production of allergic cytokines and the disease phenotype. Our findings thus reveal unexpected complexity in the miRNAome underlying allergic lung disease and demonstrate a proinflammatory role for let-7 miRNAs.

H19X-encoded miR-322(424)/miR-503 regulates muscle mass by targeting translation initiation factors.

BACKGROUND: Skeletal muscle atrophy is a debilitating complication of many chronic diseases, disuse conditions, and ageing. Genome-wide gene expression analyses have identified that elevated levels of microRNAs encoded by the H19X locus are among the most significant changes in skeletal muscles in a wide scope of human cachectic conditions. We have previously reported that the H19X locus is important for the establishment of striated muscle fate during embryogenesis. However, the role of H19X-encoded microRNAs in regulating skeletal mass in adults is unknown. METHODS: We have created a transgenic mouse strain in which ectopic expression of miR-322/miR-503 is driven by the skeletal muscle-specific muscle creatine kinase promoter. We also used an H19X mutant mouse strain in which transcription from the locus is interrupted by a gene trap. Animal phenotypes were analysed by standard histological methods. Underlying mechanisms were explored by using transcriptome profiling and validated in the two animal models and cultured myotubes. RESULTS: Our results demonstrate that the levels of H19X microRNAs are inversely related to postnatal skeletal muscle growth. Targeted overexpression of miR-322/miR-503 impeded skeletal muscle growth. The weight of gastrocnemius muscles of transgenic mice was only 54.5% of the counterparts of wild-type littermates. By contrast, interruption of transcription from the H19X locus stimulates postnatal muscle growth by 14.4-14.9% and attenuates the loss of skeletal muscle mass in response to starvation by 12.8-21.0%. Impeded muscle growth was not caused by impaired IGF1/AKT/mTOR signalling or a hyperactive ubiquitin-proteasome system, instead accompanied by markedly dropped abundance of translation initiation factors in transgenic mice. miR-322/miR-503 directly targets eIF4E, eIF4G1, eIF4B, eIF2B5, and eIF3M. CONCLUSIONS: Our study illustrates a novel pathway wherein H19X microRNAs regulate skeletal muscle growth and atrophy through regulating the abundance of translation initiation factors, thereby protein synthesis. The study highlights how translation initiation factors lie at the crux of multiple signalling pathways that control skeletal muscle mass.